One potential source of biofuels is to generate molecules from algae that are suitable for making fuels. For example, algae, like plants, can generate lipid molecules. Some lipid molecules have a general structure and molecular weight suitable for making diesel fuel additives such as fatty acid methyl ester (FAME). It is also possible to refine certain algae lipids into conventional fuels or fuel blending stocks including gasoline, diesel, and jet fuel. However, many challenges remain in developing commercial scale production techniques for biofuels based on algae production.
One challenge in further investigating algae based biofuels is identifying algae that will grow effectively in different commercial environments. In a conventional commercial production setting, algae are grown in ponds or other bodies of water that are directly or indirectly impacted by a number of external environmental variables, such as sunlight and ambient temperature. By contrast, typical conventional laboratory settings for studying algae involve little or no exposure to external variables. This reduced exposure to external variables is based on a general desire to screen algae using fixed methods that, are repeatable over many test samples. However, conventional methods for introducing this repeatability can lead to laboratory conditions that are not representative of a commercial production environment.
Previous methods for designing photobioreactors have involved using three-dimensional computational modeling of the reaction environment in a photobioreactor. For example, a photobioreactor geometry can be used as a starting point for designing computational fluid dynamic simulations. Based on the photobioreactor geometry, the fluid flow within the photobioreactor can then be modeled to generate trajectories for the movement of algae within the photobioreactor. These simulated trajectories can then be used in combination with a light attenuation model, such as Beer's Law, and a photosynthesis model, to provide simulations that predict algae growth under various conditions. Examples of this type of work include “Simulation of Microalgae Growth in Limiting Light Conditions: Flow Effect” (Pruvost et al., AIChE Journal, Vol. 48, No. 5, p 1109, 2002); “Development of virtual photobioreactor for microalgae culture considering turbulent flow and flashing light effect” (Sato et al., Energy Conservation and Management, Vol. 51, p 1196, 2010); “Scale-down of microalgae cultivations in tubular photobioreactors—A conceptual approach” (Sastre et al., Journal of Biotechnology, Vol. 132, p 127, 2007); and Analyzing and Modeling of Photobioreactors by Combining First Principles of Physiology and Hydrology (Luo et al., Biotechnology and Bioengineering, Vol. 85, p 382, 2004).
In PCT International Application Publication WO/2006/020177, systems and methods are described for growing algae in a photobioreactor system. The methods include using computational fluid dynamics to calculate trajectories of algae particles in a photobioreactor. Models of photosynthetic behavior for algae are then used to determine desired amounts of light exposure for the algae in the photobioreactor. When algae are introduced into the photobioreactor, the schedule for light exposure is set based on the predictions from the photosynthesis model.